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Cross-Phenotype Association Analysis Using Summary Statistics from GWAS

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Statistical Human Genetics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1666))

Abstract

For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice.

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Acknowledgment

This work was supported by a grant from National Heart Genome Research Institute (HG003054).

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Authors and Affiliations

  1. Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA

    Xiaoyin Li & Xiaofeng Zhu

Authors
  1. Xiaoyin Li
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  2. Xiaofeng Zhu
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Corresponding author

Correspondence to Xiaoyin Li .

Editor information

Editors and Affiliations

  1. Case Western Reserve University, Cleveland, OH, USA

    Robert C. Elston

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Li, X., Zhu, X. (2017). Cross-Phenotype Association Analysis Using Summary Statistics from GWAS. In: Elston, R. (eds) Statistical Human Genetics. Methods in Molecular Biology, vol 1666. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7274-6_22

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  • DOI: https://doi.org/10.1007/978-1-4939-7274-6_22

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7273-9

  • Online ISBN: 978-1-4939-7274-6

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